A critical aspect of the bacterial stress response is the reprogramming of RNA polymerase (RNAP) by changing the sigma factor, a subunit that determines promoter specificity. Substituting one sigma factor with another in the RNAP holoenzyme results in the expression of hitherto silent cellular functions. These newly expressed functions lead to the metabolic, morphological, and physiological changes that are required for bacterial adaptation to the new environmental conditions and survival. The ability of the human pathogen Mycobacterium tuberculosis to establish and transmit infection is tightly associated with the extensive gene expression reprogramming that allows this microorganism to switch between replicating (growth) and nonreplicating (dormancy) states during infection. The need for complex gene regulation is reflected in the highest accessory sigma factor/genome size ratio seen in M. tuberculosis among obligate pathogens. The premise of our work is that understanding the stress response of M. tuberculosis requires elucidating the biology of this microorganism's sigma factors. Central to sigma factor biology are the regulatory interactions among sigma factors, which are transcriptional and post-transcriptional. In the present R21 application, we propose to reconstruct the architecture of the sigma factor transcriptional network and to test the network response to stress conditions. The experimental approach is built around three aims, which are characterized by the increasing complexity of the network tested. These are: (1) to identify direct regulatory interactions between M. tuberculosis sigma factors expressed in E. coli, (2) to assess the effect of stress-response independent activation of sigma factor genes on the expression of downstream sigma factor genes in M. tuberculosis, and (3) to characterize the sigma network response following exposure to bacteriostatic stressors. Successful completion of the aims will enable us to identify all the direct and indirect transcriptional interactions between sigma factors and to generate hypotheses toward future reconstruction of a larger, multi-scale regulatory network that will include post-transcriptional control of sigma factor activity and the involvement of additional classes of stress-sensing and regulatory factors. Full knowledge of the sigma factor network is expected to identify sigma factors that are part of the developmental switch between growth and dormancy. These sigma factors (and the regulatory pathways controlling their expression and activity) become potential targets for anti-tuberculosis intervention.